Passive ranging is an important area of applied computer vision research. In particular, many systems need methods to estimate ranges of objects from on-board sensors. Important applications of passive ranging methods exist where using active ranging methods is impossible or impractical, such as where power limitations or covert operations restrict active system operation. For example, passive ranging methods have significant application in the nap-of-the-earth helicopter guidance systems and for systems that perform autonomous planetary missions. See B. Sridhar & A. V. Phatak, "Simulation and Analysis of Image-Based Navigation System Rotorcraft Low-Altitude Flight," AHS National Specialist's Meeting on Automation Applications of Rotorcraft, Atlanta, Ga., Apr. 4-6, 1988.
A large number of computer vision researchers have considered the use of a visual image sequences to estimate object structures and sensor motion. See, e.g., J. K. Aggarwhal & W. N. Martin, "Dynamic Scene Analysis," in Image Sequence Processing and Dynamic Scene Analysis,ed. T. S. Huang, pp. 40-74 (Springer-Verlog 1983). By using a sequence of images, known methods may not only estimate the range of the objects from the sensor, but also some methods and systems can compute the motion of the sensor. J. K. Aggarwhal & N. Nandhakumer, "On the Computation of Motion from a Sequence of Images--A Review," Proceedings of the IEEE, pp. 917-35 (August 1988) (hereinafter "Aggarwhal"), for example, discusses various approaches for computing motion from a sequence of images.
Motion analysis techniques may be divided into two general categories: (1) optical-flow-based methods, and (2) feature-based methods. The optical-flow-based approaches compute an optic flow or two-dimensional field of instantaneous velocities of the gray values in the image plane. These methods then use the optic flow along with additional scene constraints to compute the structure of the scene and the motion of the sensor. See, e.g., B. G. Schunck "Image Flow: Fundamentals and Algorithms," in Motion Understanding: Robot and Human Vision, eds. W. N. Martin & J. K. Aggarwhal (Norwell, Mass. 1988).
The feature-based methods extract a relatively sparse set of features of interest such as edges, lines, regions, etc., using a two-dimensional feature detector from the images. These methods then establish inter-frame correspondences between the features. Using constraints such as rigid-body motion, these methods compute sensor motion as a function of the correspondences between the three-dimensional structures that give rise to the features.
With the advent of accurate position determination systems such as Inertial Navigation Systems (INS) and Global Positioning Systems (GPS), it is now possible to compute quite precisely the motion of the sensor in the environment. Certain passive ranging methods use the known motion and only concentrate on computing the range of the objects of interest from the sensor. Aggarwhal, for example, describes these types of methods.
The feature-based methods that establish correspondences between sequential images of local features generally employ either intensity-based or token-based correspondence techniques. The intensity-based correspondence techniques assume that if the time difference between the generation of the two images is small, the intensity of the image of a specific feature is likely to be same in both the images. This assumption is also known as the intensity-constancy constraint. The intensity-based correspondence techniques may be further classified into gradient-based schemes and correlation-based schemes. Variations of intensity-based correspondence techniques appear, for example, in B. K. P. Horn, Robert Vision, Cambridge, Mass. 1986); and J. 0. Limb & J. A. Murphy, "Estimating the Velocity of Moving Images in Television Signals," Computer Graphics and Image Processing, Vol. 4, pp. 311-27 (1975).
Token-based correspondence techniques try to avoid the problems that arise when the intensity-based correspondence techniques violate an intensity-constancy constraint. Token-based correspondence techniques extract stable symbolic tokens from the images and attempt to match these tokens, instead of directly matching the intensities. The various tokens may have different degrees of complexity. Edges, lines, corners, and texture markings are some examples of the tokens that the token-based correspondence techniques use. See, e.g., J. Roach & J. K. Aggarwhal, "Determining the Movement of Objects from a Sequence of Images," IEEE Trans. on PAMI, pp. 554-62 (1980).
The above methods suffer from a significant limitation in that when the motion of the sensor is known a priori, the methods do not effectively use this information to extract features or establish correspondences. Both the intensity-based and the token-based correspondence techniques essentially depend only on the local grey scale changes. As a result, when the sensor moves considerably, the object image may change significantly.
When object images change significantly, the tokens that the token-based correspondence techniques extract for matching may be significantly different from previously extracted tokens. If sequential tokens are significantly different, correspondence procedures will not be reliable and resulting range estimates will be erroneous. For example, R. Dutta & M. A. Synder, "Robustness of Structure from Binocular Known Motion," IEEE Workshop on Visual Motion, (Princeton 1991) shows that small errors in correspondence may lead to large errors in the inferred motion and large errors in inferred depth. Even in the case when the motion is known a priori, small errors in image displacement may lead to large errors in depth for points more that a few multiples of the baseline from the sensor.
Similar problems plague the intensity-based methods. For example, with considerable sensor movements, object grey-value distributions in sequential images may be considerably different from the previous ones. This, again, will cause unreliable correspondence measurements and potentially erroneous range estimates.
In most passive ranging applications, the motion of the sensor is known a priori to a reasonable degree of accuracy. This knowledge of sensor motion may be used effectively to establish image correspondences and to improve range estimates. No known method or system effectively uses this information.
Thus, there is need for a method and system that overcomes many of the limitations associated with known passive ranging methods and system.
There is a need for a method and system that avoids the problems of known optic-flow and feature based passive ranging methods and systems.
A need exists for a method and system that provide, image-sequence-based target tracking and range estimation without the limitations of known intensity-based or token-based correspondence techniques.
There is a further need for a method and system for target tracking and passive range estimation that effectively uses available knowledge of sensor motion to establish image correspondences and to improve range estimates.